As consumer demand for high data rate applications, such as streaming video, expands, technology providers are forced to adopt new technologies to provide the necessary bandwidth. Multiple-Input Multiple-Output (“MIMO”) is an advanced radio system that employs multiple transmit antennas and multiple receive antennas to simultaneously transmit multiple parallel data streams. Relative to previous wireless technologies, MIMO enables substantial gains in both system capacity and transmission reliability without requiring an increase in frequency resources.
MIMO systems exploit differences in the paths between transmit and receive antennas to increase data throughput and diversity. As the number of transmit and receive antennas is increased, the capacity of a MIMO channel increases linearly, and the probability of all sub-channels between the transmitter and receiver simultaneously fading decreases exponentially. As might be expected, however, there is a price associated with realization of these benefits. Recovery of transmitted information in a MIMO system becomes increasingly complex with the addition of transmit antennas. This becomes particularly true in MIMO orthogonal frequency-division multiplexing (OFDM) systems. Such systems employ a digital multi-carrier modulation scheme using numerous orthogonal sub-carriers.
Many multiple-input multiple-output (MIMO) detection algorithms have been previously proposed in the literature. The optimal algorithm is conceptually simple, but is often impractical due to the fact that its complexity increases exponentially with the number of channel inputs and alphabet size. As a result, many algorithms have been proposed to solve the problem with less complexity, with the unfortunate effect of also significantly sacrificing performance.
Many MIMO detectors have been proposed and implemented as exclusively hard detectors that only give the final estimate of the channel input. Most notable is the sphere decoding detector because it can achieve Max-Log performance in an uncoded system with much less complexity on average. A summary of many MIMO detectors may be found in D. W. Waters, “Signal Detection Strategies and Algorithms for multiple-Input Multiple-Output Channels”, Georgia Institute of Technology, PhD dissertation, December 2005, including many variations of the sphere detector that minimize complexity without sacrificing performance. At least one list-sphere detector computes the log-likelihood ratio (LLR) for a channel input. Unfortunately, implementing a list-sphere detector is still quite complex, requiring significant processing resources.
Improvements are desired to achieve a favorable performance-complexity trade-off compared to existing MIMO detectors.